Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations2205
Missing cells0
Missing cells (%)0.0%
Duplicate rows179
Duplicate rows (%)8.1%
Total size in memory381.3 KiB
Average record size in memory177.1 B

Variable types

Categorical11
Numeric11

Alerts

Dataset has 179 (8.1%) duplicate rowsDuplicates
AcceptedCmpTotal is highly overall correlated with HasAcceptedCmpHigh correlation
Age is highly overall correlated with AgeGroupHigh correlation
AgeGroup is highly overall correlated with AgeHigh correlation
Children is highly overall correlated with HasChildrenHigh correlation
Days_Since_Enrolled is highly overall correlated with Dt_Customer_Quarter and 1 other fieldsHigh correlation
Dt_Customer_Month is highly overall correlated with Dt_Customer_QuarterHigh correlation
Dt_Customer_Quarter is highly overall correlated with Days_Since_Enrolled and 1 other fieldsHigh correlation
HasAcceptedCmp is highly overall correlated with AcceptedCmpTotalHigh correlation
HasChildren is highly overall correlated with Children and 5 other fieldsHigh correlation
Income is highly overall correlated with HasChildren and 5 other fieldsHigh correlation
MntGoldProds is highly overall correlated with Income and 3 other fieldsHigh correlation
MntRegularProds is highly overall correlated with HasChildren and 4 other fieldsHigh correlation
MntTotal is highly overall correlated with HasChildren and 4 other fieldsHigh correlation
NumDealsPurchases is highly overall correlated with HasChildrenHigh correlation
NumTotalPurchases is highly overall correlated with Income and 3 other fieldsHigh correlation
NumWebVisitsMonth is highly overall correlated with HasChildren and 1 other fieldsHigh correlation
Years_Since_Enrolled is highly overall correlated with Days_Since_EnrolledHigh correlation
Complain is highly imbalanced (92.5%)Imbalance
AcceptedCmpTotal is highly imbalanced (57.0%)Imbalance
Recency has 28 (1.3%) zerosZeros
NumDealsPurchases has 39 (1.8%) zerosZeros
MntGoldProds has 61 (2.8%) zerosZeros

Reproduction

Analysis started2025-11-27 18:00:12.910146
Analysis finished2025-11-27 18:00:25.935439
Duration13.03 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
Graduation
1113 
PhD
476 
Master
364 
2n Cycle
198 
Basic
 
54

Length

Max length10
Median length10
Mean length7.5265306
Min length3

Characters and Unicode

Total characters16596
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPhD

Common Values

ValueCountFrequency (%)
Graduation1113
50.5%
PhD476
21.6%
Master364
 
16.5%
2n Cycle198
 
9.0%
Basic54
 
2.4%

Length

2025-11-27T15:00:26.187687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:26.301882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduation1113
46.3%
phd476
19.8%
master364
 
15.1%
2n198
 
8.2%
cycle198
 
8.2%
basic54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a2644
15.9%
r1477
8.9%
t1477
8.9%
n1311
 
7.9%
i1167
 
7.0%
G1113
 
6.7%
u1113
 
6.7%
d1113
 
6.7%
o1113
 
6.7%
e562
 
3.4%
Other values (12)3506
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)16596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2644
15.9%
r1477
8.9%
t1477
8.9%
n1311
 
7.9%
i1167
 
7.0%
G1113
 
6.7%
u1113
 
6.7%
d1113
 
6.7%
o1113
 
6.7%
e562
 
3.4%
Other values (12)3506
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2644
15.9%
r1477
8.9%
t1477
8.9%
n1311
 
7.9%
i1167
 
7.0%
G1113
 
6.7%
u1113
 
6.7%
d1113
 
6.7%
o1113
 
6.7%
e562
 
3.4%
Other values (12)3506
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2644
15.9%
r1477
8.9%
t1477
8.9%
n1311
 
7.9%
i1167
 
7.0%
G1113
 
6.7%
u1113
 
6.7%
d1113
 
6.7%
o1113
 
6.7%
e562
 
3.4%
Other values (12)3506
21.1%

Marital_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
Partner
1422 
Single
783 

Length

Max length7
Median length7
Mean length6.644898
Min length6

Characters and Unicode

Total characters14652
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowPartner
4th rowPartner
5th rowPartner

Common Values

ValueCountFrequency (%)
Partner1422
64.5%
Single783
35.5%

Length

2025-11-27T15:00:26.450647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:26.541190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
partner1422
64.5%
single783
35.5%

Most occurring characters

ValueCountFrequency (%)
r2844
19.4%
n2205
15.0%
e2205
15.0%
P1422
9.7%
a1422
9.7%
t1422
9.7%
S783
 
5.3%
i783
 
5.3%
g783
 
5.3%
l783
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)14652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r2844
19.4%
n2205
15.0%
e2205
15.0%
P1422
9.7%
a1422
9.7%
t1422
9.7%
S783
 
5.3%
i783
 
5.3%
g783
 
5.3%
l783
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r2844
19.4%
n2205
15.0%
e2205
15.0%
P1422
9.7%
a1422
9.7%
t1422
9.7%
S783
 
5.3%
i783
 
5.3%
g783
 
5.3%
l783
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r2844
19.4%
n2205
15.0%
e2205
15.0%
P1422
9.7%
a1422
9.7%
t1422
9.7%
S783
 
5.3%
i783
 
5.3%
g783
 
5.3%
l783
 
5.3%

Children
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
1
1112 
0
628 
2
415 
3
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2205
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11112
50.4%
0628
28.5%
2415
 
18.8%
350
 
2.3%

Length

2025-11-27T15:00:26.647244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:26.743414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11112
50.4%
0628
28.5%
2415
 
18.8%
350
 
2.3%

Most occurring characters

ValueCountFrequency (%)
11112
50.4%
0628
28.5%
2415
 
18.8%
350
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11112
50.4%
0628
28.5%
2415
 
18.8%
350
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11112
50.4%
0628
28.5%
2415
 
18.8%
350
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11112
50.4%
0628
28.5%
2415
 
18.8%
350
 
2.3%

HasChildren
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
1
1577 
0
628 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11577
71.5%
0628
 
28.5%

Length

2025-11-27T15:00:26.858027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:26.935508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11577
71.5%
0628
 
28.5%

Most occurring characters

ValueCountFrequency (%)
11577
71.5%
0628
 
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11577
71.5%
0628
 
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11577
71.5%
0628
 
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11577
71.5%
0628
 
28.5%

Age
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.095692
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:27.065636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q137
median44
Q355
95-th percentile64
Maximum74
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.705801
Coefficient of variation (CV)0.25957692
Kurtosis-0.79703564
Mean45.095692
Median Absolute Deviation (MAD)9
Skewness0.08994081
Sum99436
Variance137.02578
MonotonicityNot monotonic
2025-11-27T15:00:27.222809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3888
 
4.0%
4385
 
3.9%
3982
 
3.7%
4278
 
3.5%
3676
 
3.4%
4475
 
3.4%
4974
 
3.4%
4171
 
3.2%
4570
 
3.2%
4069
 
3.1%
Other values (46)1437
65.2%
ValueCountFrequency (%)
182
 
0.1%
195
 
0.2%
203
 
0.1%
215
 
0.2%
2213
0.6%
2315
0.7%
2418
0.8%
2529
1.3%
2629
1.3%
2727
1.2%
ValueCountFrequency (%)
741
 
< 0.1%
731
 
< 0.1%
716
 
0.3%
707
 
0.3%
698
 
0.4%
6816
0.7%
6716
0.7%
6621
1.0%
6529
1.3%
6429
1.3%

AgeGroup
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
31-45
958 
46-60
727 
61+
263 
18-30
257 

Length

Max length5
Median length5
Mean length4.7614512
Min length3

Characters and Unicode

Total characters10499
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46-60
2nd row46-60
3rd row46-60
4th row18-30
5th row31-45

Common Values

ValueCountFrequency (%)
31-45958
43.4%
46-60727
33.0%
61+263
 
11.9%
18-30257
 
11.7%

Length

2025-11-27T15:00:27.373898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:27.476901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
31-45958
43.4%
46-60727
33.0%
61263
 
11.9%
18-30257
 
11.7%

Most occurring characters

ValueCountFrequency (%)
-1942
18.5%
61717
16.4%
41685
16.0%
11478
14.1%
31215
11.6%
0984
9.4%
5958
9.1%
+263
 
2.5%
8257
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-1942
18.5%
61717
16.4%
41685
16.0%
11478
14.1%
31215
11.6%
0984
9.4%
5958
9.1%
+263
 
2.5%
8257
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-1942
18.5%
61717
16.4%
41685
16.0%
11478
14.1%
31215
11.6%
0984
9.4%
5958
9.1%
+263
 
2.5%
8257
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-1942
18.5%
61717
16.4%
41685
16.0%
11478
14.1%
31215
11.6%
0984
9.4%
5958
9.1%
+263
 
2.5%
8257
 
2.4%

Income
Real number (ℝ)

High correlation 

Distinct1963
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51622.095
Minimum1730
Maximum113734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:27.619402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18980
Q135196
median51287
Q368281
95-th percentile83829
Maximum113734
Range112004
Interquartile range (IQR)33085

Descriptive statistics

Standard deviation20713.064
Coefficient of variation (CV)0.40124416
Kurtosis-0.84756387
Mean51622.095
Median Absolute Deviation (MAD)16463
Skewness0.013164263
Sum1.1382672 × 108
Variance4.2903101 × 108
MonotonicityNot monotonic
2025-11-27T15:00:27.783975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750012
 
0.5%
358604
 
0.2%
189293
 
0.1%
484323
 
0.1%
186903
 
0.1%
674453
 
0.1%
838443
 
0.1%
399223
 
0.1%
341763
 
0.1%
377603
 
0.1%
Other values (1953)2165
98.2%
ValueCountFrequency (%)
17301
< 0.1%
24471
< 0.1%
35021
< 0.1%
40231
< 0.1%
44281
< 0.1%
48611
< 0.1%
53051
< 0.1%
56481
< 0.1%
65601
< 0.1%
68351
< 0.1%
ValueCountFrequency (%)
1137341
< 0.1%
1054711
< 0.1%
1026921
< 0.1%
1021601
< 0.1%
1019701
< 0.1%
987772
0.1%
968761
< 0.1%
968431
< 0.1%
965471
< 0.1%
955291
< 0.1%

Recency
Real number (ℝ)

Zeros 

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.00907
Minimum0
Maximum99
Zeros28
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:27.943366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.932111
Coefficient of variation (CV)0.59034198
Kurtosis-1.1984429
Mean49.00907
Median Absolute Deviation (MAD)25
Skewness-0.0018740372
Sum108065
Variance837.06707
MonotonicityNot monotonic
2025-11-27T15:00:28.108700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5637
 
1.7%
3032
 
1.5%
5432
 
1.5%
4631
 
1.4%
9230
 
1.4%
6530
 
1.4%
2929
 
1.3%
7129
 
1.3%
4929
 
1.3%
329
 
1.3%
Other values (90)1897
86.0%
ValueCountFrequency (%)
028
1.3%
124
1.1%
228
1.3%
329
1.3%
426
1.2%
515
0.7%
621
1.0%
712
0.5%
825
1.1%
924
1.1%
ValueCountFrequency (%)
9916
0.7%
9821
1.0%
9720
0.9%
9623
1.0%
9518
0.8%
9426
1.2%
9321
1.0%
9230
1.4%
9118
0.8%
9020
0.9%

Complain
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
0
2185 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02185
99.1%
120
 
0.9%

Length

2025-11-27T15:00:28.231432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:28.276639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02185
99.1%
120
 
0.9%

Most occurring characters

ValueCountFrequency (%)
02185
99.1%
120
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02185
99.1%
120
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02185
99.1%
120
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02185
99.1%
120
 
0.9%

Dt_Customer_Month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4675737
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:28.311330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4946765
Coefficient of variation (CV)0.5403381
Kurtosis-1.2807731
Mean6.4675737
Median Absolute Deviation (MAD)3
Skewness0.0017519562
Sum14261
Variance12.212764
MonotonicityNot monotonic
2025-11-27T15:00:28.353493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8217
9.8%
5212
9.6%
10210
9.5%
3208
9.4%
1195
8.8%
9189
8.6%
11183
8.3%
4180
8.2%
2176
8.0%
12175
7.9%
Other values (2)260
11.8%
ValueCountFrequency (%)
1195
8.8%
2176
8.0%
3208
9.4%
4180
8.2%
5212
9.6%
6160
7.3%
7100
4.5%
8217
9.8%
9189
8.6%
10210
9.5%
ValueCountFrequency (%)
12175
7.9%
11183
8.3%
10210
9.5%
9189
8.6%
8217
9.8%
7100
4.5%
6160
7.3%
5212
9.6%
4180
8.2%
3208
9.4%

Dt_Customer_Quarter
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
1
579 
4
568 
2
552 
3
506 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2205
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1579
26.3%
4568
25.8%
2552
25.0%
3506
22.9%

Length

2025-11-27T15:00:28.403678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:28.440576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1579
26.3%
4568
25.8%
2552
25.0%
3506
22.9%

Most occurring characters

ValueCountFrequency (%)
1579
26.3%
4568
25.8%
2552
25.0%
3506
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1579
26.3%
4568
25.8%
2552
25.0%
3506
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1579
26.3%
4568
25.8%
2552
25.0%
3506
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1579
26.3%
4568
25.8%
2552
25.0%
3506
22.9%

Days_Since_Enrolled
Real number (ℝ)

High correlation 

Distinct662
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.71837
Minimum0
Maximum699
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:28.495286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1180
median356
Q3529
95-th percentile667
Maximum699
Range699
Interquartile range (IQR)349

Descriptive statistics

Standard deviation202.56365
Coefficient of variation (CV)0.57266929
Kurtosis-1.2028565
Mean353.71837
Median Absolute Deviation (MAD)175
Skewness-0.019176487
Sum779949
Variance41032.031
MonotonicityNot monotonic
2025-11-27T15:00:28.567514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66712
 
0.5%
4811
 
0.5%
50011
 
0.5%
65511
 
0.5%
31310
 
0.5%
3810
 
0.5%
859
 
0.4%
989
 
0.4%
5439
 
0.4%
6089
 
0.4%
Other values (652)2104
95.4%
ValueCountFrequency (%)
02
 
0.1%
13
0.1%
23
0.1%
34
0.2%
45
0.2%
52
 
0.1%
62
 
0.1%
75
0.2%
82
 
0.1%
92
 
0.1%
ValueCountFrequency (%)
6991
 
< 0.1%
6981
 
< 0.1%
6974
0.2%
6963
0.1%
6955
0.2%
6943
0.1%
6931
 
< 0.1%
6923
0.1%
6914
0.2%
6907
0.3%

Years_Since_Enrolled
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
0
1138 
1
1067 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01138
51.6%
11067
48.4%

Length

2025-11-27T15:00:28.626550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:28.656901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01138
51.6%
11067
48.4%

Most occurring characters

ValueCountFrequency (%)
01138
51.6%
11067
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01138
51.6%
11067
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01138
51.6%
11067
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01138
51.6%
11067
48.4%

NumDealsPurchases
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3183673
Minimum0
Maximum15
Zeros39
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:28.689760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8861067
Coefficient of variation (CV)0.81354954
Kurtosis8.1866714
Mean2.3183673
Median Absolute Deviation (MAD)1
Skewness2.3123687
Sum5112
Variance3.5573984
MonotonicityNot monotonic
2025-11-27T15:00:28.736480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1957
43.4%
2493
22.4%
3293
 
13.3%
4187
 
8.5%
594
 
4.3%
660
 
2.7%
739
 
1.8%
039
 
1.8%
814
 
0.6%
98
 
0.4%
Other values (5)21
 
1.0%
ValueCountFrequency (%)
039
 
1.8%
1957
43.4%
2493
22.4%
3293
 
13.3%
4187
 
8.5%
594
 
4.3%
660
 
2.7%
739
 
1.8%
814
 
0.6%
98
 
0.4%
ValueCountFrequency (%)
155
 
0.2%
133
 
0.1%
123
 
0.1%
115
 
0.2%
105
 
0.2%
98
 
0.4%
814
 
0.6%
739
1.8%
660
2.7%
594
4.3%

NumWebVisitsMonth
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3369615
Minimum0
Maximum20
Zeros6
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:28.786329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4135354
Coefficient of variation (CV)0.45223025
Kurtosis1.9043985
Mean5.3369615
Median Absolute Deviation (MAD)2
Skewness0.22999442
Sum11768
Variance5.8251532
MonotonicityNot monotonic
2025-11-27T15:00:28.840985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7387
17.6%
8340
15.4%
6334
15.1%
5278
12.6%
4216
9.8%
3203
9.2%
2201
9.1%
1146
 
6.6%
982
 
3.7%
06
 
0.3%
Other values (6)12
 
0.5%
ValueCountFrequency (%)
06
 
0.3%
1146
 
6.6%
2201
9.1%
3203
9.2%
4216
9.8%
5278
12.6%
6334
15.1%
7387
17.6%
8340
15.4%
982
 
3.7%
ValueCountFrequency (%)
203
 
0.1%
192
 
0.1%
171
 
< 0.1%
142
 
0.1%
131
 
< 0.1%
103
 
0.1%
982
 
3.7%
8340
15.4%
7387
17.6%
6334
15.1%

NumTotalPurchases
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.569615
Minimum0
Maximum32
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:28.900960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median12
Q318
95-th percentile24
Maximum32
Range32
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1803499
Coefficient of variation (CV)0.57124663
Kurtosis-1.1272874
Mean12.569615
Median Absolute Deviation (MAD)6
Skewness0.29247008
Sum27716
Variance51.557425
MonotonicityNot monotonic
2025-11-27T15:00:28.955317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4200
 
9.1%
6188
 
8.5%
5178
 
8.1%
7126
 
5.7%
3124
 
5.6%
18102
 
4.6%
1698
 
4.4%
1497
 
4.4%
1788
 
4.0%
2185
 
3.9%
Other values (23)919
41.7%
ValueCountFrequency (%)
04
 
0.2%
14
 
0.2%
21
 
< 0.1%
3124
5.6%
4200
9.1%
5178
8.1%
6188
8.5%
7126
5.7%
850
 
2.3%
944
 
2.0%
ValueCountFrequency (%)
323
 
0.1%
312
 
0.1%
302
 
0.1%
295
 
0.2%
289
 
0.4%
2722
 
1.0%
2624
 
1.1%
2539
1.8%
2452
2.4%
2363
2.9%

MntRegularProds
Real number (ℝ)

High correlation 

Distinct897
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean562.76463
Minimum4
Maximum2491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:29.016501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile18
Q156
median343
Q3964
95-th percentile1697.2
Maximum2491
Range2487
Interquartile range (IQR)908

Descriptive statistics

Standard deviation575.93691
Coefficient of variation (CV)1.0234064
Kurtosis-0.21852684
Mean562.76463
Median Absolute Deviation (MAD)310
Skewness0.91581093
Sum1240896
Variance331703.33
MonotonicityNot monotonic
2025-11-27T15:00:29.083881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3930
 
1.4%
4125
 
1.1%
1624
 
1.1%
4024
 
1.1%
1924
 
1.1%
4420
 
0.9%
2019
 
0.9%
3217
 
0.8%
3716
 
0.7%
1716
 
0.7%
Other values (887)1990
90.2%
ValueCountFrequency (%)
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
87
0.3%
96
0.3%
105
0.2%
117
0.3%
128
0.4%
136
0.3%
ValueCountFrequency (%)
24911
< 0.1%
24292
0.1%
23042
0.1%
22621
< 0.1%
22441
< 0.1%
21881
< 0.1%
21691
< 0.1%
21581
< 0.1%
21571
< 0.1%
21531
< 0.1%

MntGoldProds
Real number (ℝ)

High correlation  Zeros 

Distinct212
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.057143
Minimum0
Maximum321
Zeros61
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:29.375289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median25
Q356
95-th percentile164.6
Maximum321
Range321
Interquartile range (IQR)47

Descriptive statistics

Standard deviation51.736211
Coefficient of variation (CV)1.1742979
Kurtosis3.1437592
Mean44.057143
Median Absolute Deviation (MAD)19
Skewness1.8344675
Sum97146
Variance2676.6356
MonotonicityNot monotonic
2025-11-27T15:00:29.438092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
468
 
3.1%
368
 
3.1%
167
 
3.0%
563
 
2.9%
1262
 
2.8%
061
 
2.8%
260
 
2.7%
655
 
2.5%
752
 
2.4%
1049
 
2.2%
Other values (202)1600
72.6%
ValueCountFrequency (%)
061
2.8%
167
3.0%
260
2.7%
368
3.1%
468
3.1%
563
2.9%
655
2.5%
752
2.4%
839
1.8%
943
2.0%
ValueCountFrequency (%)
3211
 
< 0.1%
2911
 
< 0.1%
2621
 
< 0.1%
2491
 
< 0.1%
2481
 
< 0.1%
2471
 
< 0.1%
2461
 
< 0.1%
2451
 
< 0.1%
2422
 
0.1%
2416
0.3%

MntTotal
Real number (ℝ)

High correlation 

Distinct1045
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.82177
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2025-11-27T15:00:29.504652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median397
Q31047
95-th percentile1776.8
Maximum2525
Range2520
Interquartile range (IQR)978

Descriptive statistics

Standard deviation601.67528
Coefficient of variation (CV)0.99151895
Kurtosis-0.33506562
Mean606.82177
Median Absolute Deviation (MAD)354
Skewness0.85955152
Sum1338042
Variance362013.15
MonotonicityNot monotonic
2025-11-27T15:00:29.565902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4618
 
0.8%
2217
 
0.8%
5716
 
0.7%
5515
 
0.7%
4415
 
0.7%
3814
 
0.6%
4314
 
0.6%
2014
 
0.6%
4814
 
0.6%
3714
 
0.6%
Other values (1035)2054
93.2%
ValueCountFrequency (%)
51
 
< 0.1%
61
 
< 0.1%
83
 
0.1%
92
 
0.1%
105
0.2%
115
0.2%
122
 
0.1%
136
0.3%
143
 
0.1%
1510
0.5%
ValueCountFrequency (%)
25252
0.1%
25241
< 0.1%
24861
< 0.1%
24401
< 0.1%
23521
< 0.1%
23491
< 0.1%
23461
< 0.1%
23022
0.1%
22831
< 0.1%
22791
< 0.1%

AcceptedCmpTotal
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
0
1747 
1
322 
2
 
81
3
 
44
4
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2205
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01747
79.2%
1322
 
14.6%
281
 
3.7%
344
 
2.0%
411
 
0.5%

Length

2025-11-27T15:00:29.624677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:29.659893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01747
79.2%
1322
 
14.6%
281
 
3.7%
344
 
2.0%
411
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01747
79.2%
1322
 
14.6%
281
 
3.7%
344
 
2.0%
411
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01747
79.2%
1322
 
14.6%
281
 
3.7%
344
 
2.0%
411
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01747
79.2%
1322
 
14.6%
281
 
3.7%
344
 
2.0%
411
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01747
79.2%
1322
 
14.6%
281
 
3.7%
344
 
2.0%
411
 
0.5%

HasAcceptedCmp
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
0
1747 
1
458 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01747
79.2%
1458
 
20.8%

Length

2025-11-27T15:00:29.710079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:29.741542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01747
79.2%
1458
 
20.8%

Most occurring characters

ValueCountFrequency (%)
01747
79.2%
1458
 
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01747
79.2%
1458
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01747
79.2%
1458
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01747
79.2%
1458
 
20.8%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.5 KiB
0
1872 
1
333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01872
84.9%
1333
 
15.1%

Length

2025-11-27T15:00:29.778211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T15:00:29.814519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01872
84.9%
1333
 
15.1%

Most occurring characters

ValueCountFrequency (%)
01872
84.9%
1333
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01872
84.9%
1333
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01872
84.9%
1333
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01872
84.9%
1333
 
15.1%

Interactions

2025-11-27T15:00:23.864865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:13.926091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.622530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:15.322504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:16.932376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:18.967663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.131218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.928505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:21.663867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.331839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.168555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.950672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:13.986822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.682175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:15.504117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:17.064726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:19.134670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.211944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.992729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:21.722756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.389376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.226442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:24.076966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.053115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:15.644843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:19.304502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:21.059781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:14.110801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.794806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:15.778084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:17.588061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:19.466686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.383643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:21.128500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:22.504843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:24.458757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.171151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.852777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:15.913331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:17.725296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:19.617418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.451258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:21.193815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:21.904666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.563377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.420790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:14.240621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.924887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:16.195800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:20.586866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:22.030186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.692415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.552016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:14.382770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:15.085741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:21.401523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.093389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.749338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.618307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:25.015505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.444781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:15.142999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:16.482815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T15:00:19.900305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.723234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:21.469963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.152629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.806665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.680161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:25.142970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.506406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:15.203279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:16.630218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:18.596005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:19.968988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.791146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:21.534436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.213856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.863917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.741807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:25.282840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:14.565199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:15.262191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:16.785581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:18.794551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.038306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:20.862875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:21.597746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:22.274117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.110890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T15:00:23.805375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-27T15:00:29.860665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AcceptedCmpTotalAgeAgeGroupChildrenComplainDays_Since_EnrolledDt_Customer_MonthDt_Customer_QuarterEducationHasAcceptedCmpHasChildrenIncomeMarital_StatusMntGoldProdsMntRegularProdsMntTotalNumDealsPurchasesNumTotalPurchasesNumWebVisitsMonthRecencyResponseYears_Since_Enrolled
AcceptedCmpTotal1.0000.0560.0480.1600.0000.0140.0000.0320.0000.9990.2790.2760.0000.1100.2620.2670.0780.1630.0960.0000.4260.000
Age0.0561.0000.9030.2150.028-0.013-0.0320.0000.1130.0740.3160.2210.0890.0750.1700.1630.0890.184-0.1350.0150.0660.043
AgeGroup0.0480.9031.0000.1640.0000.0000.0000.0130.1070.0220.2130.1830.0690.0250.1170.1100.0790.1090.0880.0630.0280.000
Children0.1600.2150.1641.0000.0000.0330.0330.0180.0340.2411.0000.3370.0480.1630.3240.3240.3690.2340.3240.0300.2040.000
Complain0.0000.0280.0000.0001.0000.0540.0000.0310.0300.0000.0000.0280.0000.0000.0000.0000.0000.0240.0000.0000.0000.000
Days_Since_Enrolled0.014-0.0130.0000.0330.0541.0000.2250.8310.0460.0200.000-0.0230.0000.2260.1720.1820.2170.1580.3060.0280.2010.961
Dt_Customer_Month0.000-0.0320.0000.0330.0000.2251.0000.9990.0440.0000.000-0.0040.0000.0320.0360.0400.0240.0240.0660.0140.0810.202
Dt_Customer_Quarter0.0320.0000.0130.0180.0310.8310.9991.0000.0360.0240.0000.0430.0000.0310.0400.0570.0480.0500.0840.0320.0700.064
Education0.0000.1130.1070.0340.0300.0460.0440.0361.0000.0320.0000.1670.0000.0650.0860.0920.0000.1020.0530.0000.0940.059
HasAcceptedCmp0.9990.0740.0220.2410.0000.0200.0000.0240.0321.0000.2370.3830.0000.2060.4190.4220.1370.3000.1420.0000.3660.000
HasChildren0.2790.3160.2131.0000.0000.0000.0000.0000.0000.2371.0000.5510.0540.2550.5340.5330.5650.3530.5580.0180.2030.000
Income0.2760.2210.1830.3370.028-0.023-0.0040.0430.1670.3830.5511.0000.0320.5190.8700.860-0.1940.789-0.6410.0090.2580.000
Marital_Status0.0000.0890.0690.0480.0000.0000.0000.0000.0000.0000.0540.0321.0000.0000.0000.0000.0140.0000.0310.0530.1470.006
MntGoldProds0.1100.0750.0250.1630.0000.2260.0320.0310.0650.2060.2550.5190.0001.0000.6510.6950.0880.648-0.2680.0180.1570.179
MntRegularProds0.2620.1700.1170.3240.0000.1720.0360.0400.0860.4190.5340.8700.0000.6511.0000.996-0.0210.909-0.4830.0220.2920.145
MntTotal0.2670.1630.1100.3240.0000.1820.0400.0570.0920.4220.5330.8600.0000.6950.9961.000-0.0210.910-0.4800.0210.2950.153
NumDealsPurchases0.0780.0890.0790.3690.0000.2170.0240.0480.0000.1370.565-0.1940.0140.088-0.021-0.0211.0000.1020.3950.0080.0980.217
NumTotalPurchases0.1630.1840.1090.2340.0240.1580.0240.0500.1020.3000.3530.7890.0000.6480.9090.9100.1021.000-0.4270.0140.1640.151
NumWebVisitsMonth0.096-0.1350.0880.3240.0000.3060.0660.0840.0530.1420.558-0.6410.031-0.268-0.483-0.4800.395-0.4271.000-0.0180.1220.248
Recency0.0000.0150.0630.0300.0000.0280.0140.0320.0000.0000.0180.0090.0530.0180.0220.0210.0080.014-0.0181.0000.2090.029
Response0.4260.0660.0280.2040.0000.2010.0810.0700.0940.3660.2030.2580.1470.1570.2920.2950.0980.1640.1220.2091.0000.172
Years_Since_Enrolled0.0000.0430.0000.0000.0000.9610.2020.0640.0590.0000.0000.0000.0060.1790.1450.1530.2170.1510.2480.0290.1721.000

Missing values

2025-11-27T15:00:25.511252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-27T15:00:25.766890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EducationMarital_StatusChildrenHasChildrenAgeAgeGroupIncomeRecencyComplainDt_Customer_MonthDt_Customer_QuarterDays_Since_EnrolledYears_Since_EnrolledNumDealsPurchasesNumWebVisitsMonthNumTotalPurchasesMntRegularProdsMntGoldProdsMntTotalAcceptedCmpTotalHasAcceptedCmpResponse
0GraduationSingle005746-6058138.058093663137221529881617001
1GraduationSingle216046-6046344.038031113025421627000
2GraduationPartner004946-6071613.0260833120142073442776000
3GraduationPartner113018-3026646.026021139026648553000
4PhDPartner113331-4558293.0940111610551440715422000
5MasterPartner114746-6062513.0160932930262070214716000
6GraduationSingle114331-4555635.03401145931461756327590000
7PhDPartner112918-3033454.032052417128814623169000
8PhDPartner114031-4530351.019062388119544246001
9PhDPartner216461+5648.06803110801201361349110
EducationMarital_StatusChildrenHasChildrenAgeAgeGroupIncomeRecencyComplainDt_Customer_MonthDt_Customer_QuarterDays_Since_EnrolledYears_Since_EnrolledNumDealsPurchasesNumWebVisitsMonthNumTotalPurchasesMntRegularProdsMntGoldProdsMntTotalAcceptedCmpTotalHasAcceptedCmpResponse
2229GraduationPartner214231-4524434.09052420275331750000
2230GraduationSingle113018-3011012.0820314701396612384110
2231MasterSingle004431-4544802.071083677128251029201049000
2232GraduationSingle002818-3026816.050083681114319322000
2234GraduationPartner114031-4534421.081073363017321930000
2235GraduationPartner114746-6061223.0460623811251610942471341000
2236PhDPartner316861+64014.05606219077154368444110
2237GraduationSingle003331-4556981.091011155016181217241241110
2238MasterPartner115846-6069245.080111560232178261843000
2239PhDPartner216046-6052869.0400104622137815121172001

Duplicate rows

Most frequently occurring

EducationMarital_StatusChildrenHasChildrenAgeAgeGroupIncomeRecencyComplainDt_Customer_MonthDt_Customer_QuarterDays_Since_EnrolledYears_Since_EnrolledNumDealsPurchasesNumWebVisitsMonthNumTotalPurchasesMntRegularProdsMntGoldProdsMntTotalAcceptedCmpTotalHasAcceptedCmpResponse# duplicates
21GraduationPartner002418-3018929.01502149811656718850003
34GraduationPartner005546-6018690.077012454811844119600003
38GraduationPartner006261+83844.05705241311119138319115741103
45GraduationPartner113131-4539922.0300215001287120361560003
53GraduationPartner114031-4567445.0630836861562711631111740003
136MasterSingle114646-6063841.064042434116217771319080003
02n CyclePartner003231-4566664.0780936421131812573212890002
12n CyclePartner004031-4520130.09903110401842212340002
22n CyclePartner004431-4515315.02708333002564717640002
32n CyclePartner004946-6070924.041042830131912877613631102